Triple
T26399746
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cisco TelePresence Management Suite |
E663665
|
entity |
| Predicate | component |
P35
|
FINISHED |
| Object |
TMS server
TMS server is the core server component of Cisco TelePresence Management Suite that centrally manages, schedules, and monitors video conferencing infrastructure and endpoints.
|
E1722195
|
NE FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: TMS server | Statement: [Cisco TelePresence Management Suite, component, TMS server]
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: TMS server Triple: [Cisco TelePresence Management Suite, component, TMS server]
Generated description
TMS server is the core server component of Cisco TelePresence Management Suite that centrally manages, schedules, and monitors video conferencing infrastructure and endpoints.
Provenance (5 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69ee883823988190b418b111be28a44a |
completed | April 26, 2026, 9:48 p.m. |
| NER | Named-entity recognition | batch_69f610f2c09c8190849494490f255758 |
completed | May 2, 2026, 2:57 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a119a8b5ea48190bf51203a1fe4f3f6 |
completed | May 23, 2026, 12:16 p.m. |
| NEDg | Description generation | batch_6a119c7290c88190873129f6193a2121 |
completed | May 23, 2026, 12:24 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a119cf886e88190aa83f0621903f248 |
completed | May 23, 2026, 12:26 p.m. |
Created at: April 26, 2026, 11:31 p.m.